code: def _preprocess(
      self, X: pd.DataFrame, is_train=False, **kwargs
  ) -> pd.DataFrame:
      X = super()._preprocess(X, **kwargs)
      if is_train:
          self._categorical_featnames = (
              self._feature_metadata.get_features(
                  valid_raw_types=["category"]
              )
          )
          self._other_featnames = self._feature_metadata.get_features(
              invalid_raw_types=["category"]
          )
          if self._categorical_featnames:
              self._ohe = OneHotEncoder(
                  dtype=np.uint8, handle_unknown="ignore"
              )
              self._ohe.fit(X=X[self._categorical_featnames])
              self._ohe_columns = self._ohe.get_feature_names_out()
          else:
              self._ohe = None  # otherwise no model is fitted when there are not self._categorical_featnames
  
      if self._ohe is not None:
          X_index = X.index
          X_ohe = self._ohe.transform(X[self._categorical_featnames])
          X_ohe = pd.DataFrame.sparse.from_spmatrix(
              X_ohe, columns=self._ohe_columns, index=X_index
          )
          if self._other_featnames:
              X = pd.concat([X[self._other_featnames], X_ohe], axis=1)
          else:
              X = X_ohe
  
      return X.fillna(0)
  
imports: from abc import abstractmethod
  import numpy
  import pandas
  from sklearn.preprocessing import OneHotEncoder
  from autogluon.common.utils.try_import import try_import_imodels
  from autogluon.core.models import AbstractModel
label_desc: Use image node_id 4 for calling the _IModelsModel obj's underlying member method code with example usage: obj._preprocess(X, is_train) and returns: X